Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations499380
Missing cells124160
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.5 MiB
Average record size in memory188.0 B

Variable types

Numeric8
Categorical4
DateTime1

Alerts

CYCLE_NUMBER is highly overall correlated with TIME_QCHigh correlation
PRES is highly overall correlated with TEMPHigh correlation
PSAL is highly overall correlated with TEMPHigh correlation
TEMP is highly overall correlated with PRES and 1 other fieldsHigh correlation
TIME_QC is highly overall correlated with CYCLE_NUMBERHigh correlation
DIRECTION is highly imbalanced (96.3%) Imbalance
POSITION_QC is highly imbalanced (98.9%) Imbalance
TIME_QC is highly imbalanced (81.6%) Imbalance
PSAL has 62263 (12.5%) missing values Missing
TEMP has 61897 (12.4%) missing values Missing
N_POINTS is uniformly distributed Uniform
N_POINTS has unique values Unique

Reproduction

Analysis started2025-09-20 15:54:20.605380
Analysis finished2025-09-20 15:54:48.285052
Duration27.68 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

N_POINTS
Real number (ℝ)

Uniform  Unique 

Distinct499380
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249689.5
Minimum0
Maximum499379
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-09-20T15:54:48.436833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24968.95
Q1124844.75
median249689.5
Q3374534.25
95-th percentile474410.05
Maximum499379
Range499379
Interquartile range (IQR)249689.5

Descriptive statistics

Standard deviation144158.73
Coefficient of variation (CV)0.577352
Kurtosis-1.2
Mean249689.5
Median Absolute Deviation (MAD)124845
Skewness4.9281174 × 10-16
Sum1.2468994 × 1011
Variance2.078174 × 1010
MonotonicityStrictly increasing
2025-09-20T15:54:48.611401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499379 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
499363 1
 
< 0.1%
499362 1
 
< 0.1%
499361 1
 
< 0.1%
499360 1
 
< 0.1%
499359 1
 
< 0.1%
499358 1
 
< 0.1%
Other values (499370) 499370
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
499379 1
< 0.1%
499378 1
< 0.1%
499377 1
< 0.1%
499376 1
< 0.1%
499375 1
< 0.1%
499374 1
< 0.1%
499373 1
< 0.1%
499372 1
< 0.1%
499371 1
< 0.1%
499370 1
< 0.1%

CYCLE_NUMBER
Real number (ℝ)

High correlation 

Distinct239
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.35025
Minimum1
Maximum564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-09-20T15:54:48.757941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q142
median83
Q3172
95-th percentile324
Maximum564
Range563
Interquartile range (IQR)130

Descriptive statistics

Standard deviation110.10941
Coefficient of variation (CV)0.93036907
Kurtosis4.9310283
Mean118.35025
Median Absolute Deviation (MAD)44
Skewness2.0524291
Sum59101746
Variance12124.082
MonotonicityNot monotonic
2025-09-20T15:54:48.904819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 9325
 
1.9%
107 8835
 
1.8%
76 8794
 
1.8%
79 8197
 
1.6%
29 7802
 
1.6%
189 7580
 
1.5%
28 7557
 
1.5%
190 7074
 
1.4%
185 6866
 
1.4%
78 6812
 
1.4%
Other values (229) 420538
84.2%
ValueCountFrequency (%)
1 2980
0.6%
2 1972
0.4%
3 488
 
0.1%
4 978
 
0.2%
5 490
 
0.1%
6 490
 
0.1%
12 998
 
0.2%
13 3115
0.6%
14 4111
0.8%
15 4609
0.9%
ValueCountFrequency (%)
564 482
0.1%
563 482
0.1%
562 482
0.1%
561 482
0.1%
560 482
0.1%
559 482
0.1%
558 482
0.1%
557 482
0.1%
556 482
0.1%
555 482
0.1%

DATA_MODE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.8 MiB
A
270584 
R
226145 
D
 
2651

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters499380
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 270584
54.2%
R 226145
45.3%
D 2651
 
0.5%

Length

2025-09-20T15:54:49.037984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T15:54:49.439965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 270584
54.2%
r 226145
45.3%
d 2651
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 270584
54.2%
R 226145
45.3%
D 2651
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 270584
54.2%
R 226145
45.3%
D 2651
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 270584
54.2%
R 226145
45.3%
D 2651
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 270584
54.2%
R 226145
45.3%
D 2651
 
0.5%

DIRECTION
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.8 MiB
A
497440 
D
 
1940

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters499380
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 497440
99.6%
D 1940
 
0.4%

Length

2025-09-20T15:54:49.548197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T15:54:49.620771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 497440
99.6%
d 1940
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 497440
99.6%
D 1940
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 497440
99.6%
D 1940
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 497440
99.6%
D 1940
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 497440
99.6%
D 1940
 
0.4%

PLATFORM_NUMBER
Real number (ℝ)

Distinct364
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4510042.7
Minimum1901514
Maximum7902312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-09-20T15:54:49.722740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1901514
5-th percentile1902050
Q11902755
median4903783
Q35906981
95-th percentile7901117
Maximum7902312
Range6000798
Interquartile range (IQR)4004226

Descriptive statistics

Standard deviation2128322.2
Coefficient of variation (CV)0.47190734
Kurtosis-1.4855175
Mean4510042.7
Median Absolute Deviation (MAD)2000348
Skewness0.05761734
Sum2.2522251 × 1012
Variance4.5297556 × 1012
MonotonicityNot monotonic
2025-09-20T15:54:49.867939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5902490 13978
 
2.8%
2903955 7485
 
1.5%
2903956 7455
 
1.5%
5907141 7094
 
1.4%
4903839 7072
 
1.4%
1902734 5489
 
1.1%
7901023 5425
 
1.1%
3902490 5414
 
1.1%
6990504 5401
 
1.1%
6990503 4377
 
0.9%
Other values (354) 430190
86.1%
ValueCountFrequency (%)
1901514 4
 
< 0.1%
1901759 1494
0.3%
1901760 1494
0.3%
1901762 1496
0.3%
1901765 1496
0.3%
1901766 1506
0.3%
1901767 2008
0.4%
1901768 1506
0.3%
1901769 1506
0.3%
1901771 1506
0.3%
ValueCountFrequency (%)
7902312 503
 
0.1%
7902287 186
 
< 0.1%
7902274 1495
0.3%
7902251 186
 
< 0.1%
7902250 438
 
0.1%
7902249 186
 
< 0.1%
7902248 153
 
< 0.1%
7902247 186
 
< 0.1%
7902246 159
 
< 0.1%
7902244 186
 
< 0.1%

POSITION_QC
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.8 MiB
1
498882 
2
 
498

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters499380
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 498882
99.9%
2 498
 
0.1%

Length

2025-09-20T15:54:49.990192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T15:54:50.063020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 498882
99.9%
2 498
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 498882
99.9%
2 498
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 498882
99.9%
2 498
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 498882
99.9%
2 498
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 498882
99.9%
2 498
 
0.1%

PRES
Real number (ℝ)

High correlation 

Distinct75957
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean462.73376
Minimum0
Maximum999.98999
Zeros37
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-09-20T15:54:50.163497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q1201.2
median443.98001
Q3717.57001
95-th percentile939.96002
Maximum999.98999
Range999.98999
Interquartile range (IQR)516.37001

Descriptive statistics

Standard deviation294.10308
Coefficient of variation (CV)0.63557731
Kurtosis-1.2228782
Mean462.73376
Median Absolute Deviation (MAD)256.10001
Skewness0.1389846
Sum2.3107999 × 108
Variance86496.623
MonotonicityNot monotonic
2025-09-20T15:54:50.320116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 164
 
< 0.1%
6 164
 
< 0.1%
438 159
 
< 0.1%
5 159
 
< 0.1%
2 155
 
< 0.1%
4 154
 
< 0.1%
9 150
 
< 0.1%
8 149
 
< 0.1%
638 149
 
< 0.1%
338 147
 
< 0.1%
Other values (75947) 497830
99.7%
ValueCountFrequency (%)
0 37
< 0.1%
0.005 5
 
< 0.1%
0.01 4
 
< 0.1%
0.03 5
 
< 0.1%
0.032 3
 
< 0.1%
0.06 1
 
< 0.1%
0.07 3
 
< 0.1%
0.09 1
 
< 0.1%
0.1 58
< 0.1%
0.105 4
 
< 0.1%
ValueCountFrequency (%)
999.98999 1
 
< 0.1%
999.980042 1
 
< 0.1%
999.97998 1
 
< 0.1%
999.969971 2
 
< 0.1%
999.960022 52
< 0.1%
999.950012 4
 
< 0.1%
999.940002 6
 
< 0.1%
999.929993 5
 
< 0.1%
999.920044 1
 
< 0.1%
999.919983 17
 
< 0.1%

PSAL
Real number (ℝ)

High correlation  Missing 

Distinct40569
Distinct (%)9.3%
Missing62263
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean34.788406
Minimum-0.044
Maximum131.99779
Zeros135
Zeros (%)< 0.1%
Negative12
Negative (%)< 0.1%
Memory size3.8 MiB
2025-09-20T15:54:50.468564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.044
5-th percentile34.450001
Q134.7472
median34.994999
Q335.3909
95-th percentile36.025002
Maximum131.99779
Range132.04179
Interquartile range (IQR)0.6437

Descriptive statistics

Standard deviation2.8979601
Coefficient of variation (CV)0.083302467
Kurtosis126.83036
Mean34.788406
Median Absolute Deviation (MAD)0.284
Skewness-9.4832318
Sum15206604
Variance8.3981727
MonotonicityNot monotonic
2025-09-20T15:54:50.640653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 579
 
0.1%
35.001999 575
 
0.1%
35.000999 556
 
0.1%
34.999001 549
 
0.1%
35.004002 526
 
0.1%
35.002998 509
 
0.1%
35.005001 508
 
0.1%
34.998001 502
 
0.1%
34.997002 499
 
0.1%
34.993 497
 
0.1%
Other values (40559) 431817
86.5%
(Missing) 62263
 
12.5%
ValueCountFrequency (%)
-0.044 12
 
< 0.1%
0 135
< 0.1%
0.012 1
 
< 0.1%
0.1 77
< 0.1%
0.101 82
< 0.1%
0.102 110
< 0.1%
0.103 118
< 0.1%
0.104 104
< 0.1%
0.105 113
< 0.1%
0.106 130
< 0.1%
ValueCountFrequency (%)
131.997787 1
< 0.1%
131.929398 1
< 0.1%
131.613892 1
< 0.1%
131.478394 1
< 0.1%
131.403 1
< 0.1%
131.297501 1
< 0.1%
37.581001 1
< 0.1%
37.573002 1
< 0.1%
37.544998 1
< 0.1%
37.535999 1
< 0.1%

TEMP
Real number (ℝ)

High correlation  Missing 

Distinct45896
Distinct (%)10.5%
Missing61897
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean12.866534
Minimum4.087
Maximum31.777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-09-20T15:54:50.791464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.087
5-th percentile6.011
Q18.522
median11.077
Q315.94
95-th percentile25.656
Maximum31.777
Range27.69
Interquartile range (IQR)7.418

Descriptive statistics

Standard deviation5.9493922
Coefficient of variation (CV)0.46239275
Kurtosis0.30041251
Mean12.866534
Median Absolute Deviation (MAD)3.202
Skewness1.0418094
Sum5628890.1
Variance35.395268
MonotonicityNot monotonic
2025-09-20T15:54:50.946644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.884001 151
 
< 0.1%
6.48 123
 
< 0.1%
24.882 97
 
< 0.1%
24.882999 96
 
< 0.1%
24.891001 96
 
< 0.1%
6.259 83
 
< 0.1%
9.839 83
 
< 0.1%
6.079 83
 
< 0.1%
6.481 82
 
< 0.1%
9.577 81
 
< 0.1%
Other values (45886) 436508
87.4%
(Missing) 61897
 
12.4%
ValueCountFrequency (%)
4.087 1
< 0.1%
4.122 1
< 0.1%
4.136 1
< 0.1%
4.14 1
< 0.1%
4.144 1
< 0.1%
4.154 1
< 0.1%
4.168 1
< 0.1%
4.173 1
< 0.1%
4.176 1
< 0.1%
4.181 1
< 0.1%
ValueCountFrequency (%)
31.777 1
< 0.1%
31.263 1
< 0.1%
31.252001 1
< 0.1%
31.205 1
< 0.1%
31.063999 1
< 0.1%
31.042999 1
< 0.1%
31.039 1
< 0.1%
31.025 1
< 0.1%
31.017 1
< 0.1%
30.907 1
< 0.1%

TIME_QC
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.8 MiB
1
485402 
8
 
13978

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters499380
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 485402
97.2%
8 13978
 
2.8%

Length

2025-09-20T15:54:51.088991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T15:54:51.159655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 485402
97.2%
8 13978
 
2.8%

Most occurring characters

ValueCountFrequency (%)
1 485402
97.2%
8 13978
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 485402
97.2%
8 13978
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 485402
97.2%
8 13978
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 485402
97.2%
8 13978
 
2.8%

TIME
Date

Distinct1155
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
Minimum2025-08-20 01:12:05+00:00
Maximum2025-09-19 22:44:20+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-20T15:54:51.280765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:51.445790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

LATITUDE
Real number (ℝ)

Distinct1142
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.7712651
Minimum-29.989
Maximum24.73319
Zeros0
Zeros (%)0.0%
Negative344814
Negative (%)69.0%
Memory size3.8 MiB
2025-09-20T15:54:51.615161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-29.989
5-th percentile-28.003302
Q1-22.020383
median-8.6783
Q33.7272
95-th percentile23.475005
Maximum24.73319
Range54.72219
Interquartile range (IQR)25.747583

Descriptive statistics

Standard deviation16.388594
Coefficient of variation (CV)-2.420315
Kurtosis-0.95317908
Mean-6.7712651
Median Absolute Deviation (MAD)13.17392
Skewness0.45877008
Sum-3381434.4
Variance268.58603
MonotonicityNot monotonic
2025-09-20T15:54:51.766629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1215726667 1478
 
0.3%
1.052201667 1458
 
0.3%
0.7588755 1441
 
0.3%
16.919035 1436
 
0.3%
17.00319317 1424
 
0.3%
23.75949067 1421
 
0.3%
23.11384933 1417
 
0.3%
16.48735083 1409
 
0.3%
23.7540785 1408
 
0.3%
17.38521817 1405
 
0.3%
Other values (1132) 485083
97.1%
ValueCountFrequency (%)
-29.989 489
0.1%
-29.9836 548
0.1%
-29.975 489
0.1%
-29.942 490
0.1%
-29.9012 505
0.1%
-29.84117667 292
0.1%
-29.82229 505
0.1%
-29.81628 505
0.1%
-29.81226 505
0.1%
-29.783 489
0.1%
ValueCountFrequency (%)
24.73319 499
 
0.1%
24.661555 499
 
0.1%
24.61712833 499
 
0.1%
24.57424667 499
 
0.1%
24.502735 499
 
0.1%
24.49146533 1366
0.3%
24.43172667 499
 
0.1%
24.43117667 499
 
0.1%
24.41383833 499
 
0.1%
24.40533833 499
 
0.1%

LONGITUDE
Real number (ℝ)

Distinct1147
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.374088
Minimum35.69775
Maximum117.00216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-09-20T15:54:51.911786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35.69775
5-th percentile46.1713
Q160.08975
median70.24913
Q386.82716
95-th percentile104.49009
Maximum117.00216
Range81.304413
Interquartile range (IQR)26.73741

Descriptive statistics

Standard deviation17.727339
Coefficient of variation (CV)0.24160218
Kurtosis-0.75932466
Mean73.374088
Median Absolute Deviation (MAD)13.25187
Skewness0.21411904
Sum36641552
Variance314.25856
MonotonicityNot monotonic
2025-09-20T15:54:52.068377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.45284467 1478
 
0.3%
77.40757 1458
 
0.3%
77.14380583 1441
 
0.3%
59.37788617 1436
 
0.3%
58.72022183 1424
 
0.3%
61.73529967 1421
 
0.3%
61.69210183 1417
 
0.3%
66.93568717 1409
 
0.3%
61.03817733 1408
 
0.3%
58.589684 1405
 
0.3%
Other values (1137) 485083
97.1%
ValueCountFrequency (%)
35.69775 504
0.1%
35.7197 488
0.1%
35.9752 488
0.1%
36.30995 504
0.1%
36.4444 488
0.1%
37.54116 504
0.1%
38.11791 504
0.1%
38.24111 504
0.1%
38.324045 696
0.1%
38.37711667 689
0.1%
ValueCountFrequency (%)
117.0021633 56
 
< 0.1%
116.55347 55
 
< 0.1%
116.3798 56
 
< 0.1%
114.6765 498
0.1%
114.5469 498
0.1%
114.5448 498
0.1%
114.0538909 498
0.1%
114.0219424 498
0.1%
114.0083677 498
0.1%
112.192535 582
0.1%

Interactions

2025-09-20T15:54:44.458356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:31.982829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:33.892068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:35.397227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:37.103657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:38.816598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:40.600548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:42.682689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:44.672724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:32.186673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:34.083659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:35.575150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:37.339585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:38.996590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:40.853115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:42.968039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:44.857291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:32.375340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:34.265572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:35.787349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:37.555690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:39.170751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:41.137578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:43.245457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:45.052469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:32.906890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:34.449263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:35.980240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:37.777632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:39.350717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:41.406803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:43.510733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:45.242124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:33.090252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:34.636026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:36.177307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:37.988163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:39.520274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:41.658509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:43.678504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:45.440582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:33.280771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:34.817917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:36.384707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:38.210009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:40.015975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:41.896273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:43.849771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:45.627000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:33.463243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:35.018461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:36.617229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:38.426756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:40.209845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:42.168854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:44.023060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:45.803208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:33.678633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:35.202835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:36.865495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:38.614159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:40.409858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:42.404522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T15:54:44.238460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-20T15:54:52.196706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CYCLE_NUMBERDATA_MODEDIRECTIONLATITUDELONGITUDEN_POINTSPLATFORM_NUMBERPOSITION_QCPRESPSALTEMPTIME_QC
CYCLE_NUMBER1.0000.3270.083-0.270-0.0150.003-0.0960.0880.021-0.166-0.0391.000
DATA_MODE0.3271.0000.0570.3640.2280.1190.2590.0290.0360.0340.1100.156
DIRECTION0.0830.0571.0000.0940.0890.0870.1020.0000.0140.0060.0390.010
LATITUDE-0.2700.3640.0941.000-0.1890.004-0.0880.092-0.0220.3560.0930.334
LONGITUDE-0.0150.2280.089-0.1891.000-0.0230.0270.2030.016-0.326-0.1360.303
N_POINTS0.0030.1190.0870.004-0.0231.000-0.0230.0950.0070.0050.0000.088
PLATFORM_NUMBER-0.0960.2590.102-0.0880.027-0.0231.0000.052-0.042-0.0120.0140.278
POSITION_QC0.0880.0290.0000.0920.2030.0950.0521.0000.0010.0020.0310.005
PRES0.0210.0360.014-0.0220.0160.007-0.0420.0011.000-0.463-0.9370.026
PSAL-0.1660.0340.0060.356-0.3260.005-0.0120.002-0.4631.0000.6150.017
TEMP-0.0390.1100.0390.093-0.1360.0000.0140.031-0.9370.6151.0000.135
TIME_QC1.0000.1560.0100.3340.3030.0880.2780.0050.0260.0170.1351.000

Missing values

2025-09-20T15:54:46.046011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-20T15:54:46.714729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-20T15:54:47.857545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

N_POINTSCYCLE_NUMBERDATA_MODEDIRECTIONPLATFORM_NUMBERPOSITION_QCPRESPSALTEMPTIME_QCTIMELATITUDELONGITUDE
0064AA290314511.1235.51900123.68600112025-08-20 01:12:05+00:0010.9861953.97337
1164AA290314512.0435.52000023.68700012025-08-20 01:12:05+00:0010.9861953.97337
2264AA290314513.0035.52000023.69199912025-08-20 01:12:05+00:0010.9861953.97337
3364AA290314513.9635.52000023.69199912025-08-20 01:12:05+00:0010.9861953.97337
4464AA290314514.9635.52000023.69199912025-08-20 01:12:05+00:0010.9861953.97337
5564AA290314515.9635.52000023.69199912025-08-20 01:12:05+00:0010.9861953.97337
6664AA290314516.9635.52000023.69199912025-08-20 01:12:05+00:0010.9861953.97337
7764AA290314518.0035.52000023.69300112025-08-20 01:12:05+00:0010.9861953.97337
8864AA290314518.9235.52000023.69300112025-08-20 01:12:05+00:0010.9861953.97337
9964AA2903145110.2035.52000023.69300112025-08-20 01:12:05+00:0010.9861953.97337
N_POINTSCYCLE_NUMBERDATA_MODEDIRECTIONPLATFORM_NUMBERPOSITION_QCPRESPSALTEMPTIME_QCTIMELATITUDELONGITUDE
49937049937051RA19027341981.79998835.4109998.89912025-09-19 22:44:20+00:0022.18069762.012415
49937149937151RA19027341983.79998835.4100008.88912025-09-19 22:44:20+00:0022.18069762.012415
49937249937251RA19027341985.79998835.4090008.86712025-09-19 22:44:20+00:0022.18069762.012415
49937349937351RA19027341987.79998835.4070018.84212025-09-19 22:44:20+00:0022.18069762.012415
49937449937451RA19027341989.79998835.4039998.82712025-09-19 22:44:20+00:0022.18069762.012415
49937549937551RA19027341991.79998835.4039998.81912025-09-19 22:44:20+00:0022.18069762.012415
49937649937651RA19027341993.79998835.4020008.80712025-09-19 22:44:20+00:0022.18069762.012415
49937749937751RA19027341995.79998835.4010018.79912025-09-19 22:44:20+00:0022.18069762.012415
49937849937851RA19027341997.79998835.4000028.78612025-09-19 22:44:20+00:0022.18069762.012415
49937949937951RA19027341999.70001235.3979998.76912025-09-19 22:44:20+00:0022.18069762.012415